Unified Causality Analysis Based on the Degrees of Freedom
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Abstract (arXiv)
Temporally evolving systems are typically modeled by dynamic equations. A key challenge in accurate modeling is understanding the causal relationships between subsystems, as well as identifying the presence and influence of unobserved hidden drivers on the observed dynamics. This paper presents a unified method capable of identifying fundamental causal relationships between pairs of systems, whether deterministic or stochastic. Notably, the method also uncovers hidden common causes beyond the observed variables. By analyzing the degrees of freedom in the system, our approach provides a more comprehensive understanding of both causal influence and hidden confounders. This unified framework is validated through theoretical models and simulations, demonstrating its robustness and potential for broader application.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 15 of its 54 references that are in the map. First posted on arXiv 25 Oct 2024.
Builds on
15 of 54 references.
References · 15
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3 more
- 2018On Learning Causal Structures from Non-Experimental Data without Any Faithfulness Assumptioncited by 6
- 2021Characterization of causal ancestral graphs for time series with latent confounderscited by 6
- 2024Causality for Earth Science -- A Review on Time-series and Spatiotemporal Causality Methodscited by 2
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